The present invention relates generally to signal processing and analysis, and more particularly to detection and identification of gas turbine engine faults from acoustic or vibrational sensor data.
Aircraft turbine engines require regular maintenance. Even small defects in turbine components can cause malfunctions, and may pose a serious risk to aircraft safety. Proper scheduling and execution of maintenance therefore requires knowledge of even minor or incipient faults. Conventional diagnostic systems utilize a wide range of factors, including aircraft operational history, engine pressure and temperature analysis, and debris monitoring to predict and catch faults before major damage occurs. A continuing need exists for simpler, faster, and more accurate diagnostic systems which can recognize incipient turbine faults.
Gas turbine engine faults produce distinct variations in sound and vibration data, making acoustic and vibrational approaches attractive for fault identification and analysis. In practice, however, acoustic and vibrational analyses have been limited by high noise levels and complex, non-stationary signals.
Acoustic analysis has been used in testing and development of turbines to examine steady-state and ideal operational harmonics of individual components. Fault diagnosis is a far more complex task, involving non-stationary signals from multiple sources, obscured by high background noise. A variety of analytical approaches to vibrational and acoustic fault diagnosis have been attempted in the past, in both the time and frequency domains. These approaches, however, have lacked the accuracy to reliably distinguish fault signatures from background noise. Other techniques using wavelet transform or neural networks have recently been attempted with limited success, but fault detection and identification using acoustic and vibrational analyses remains an open problem.